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                <title>Data analysis - Clustering - EM</title>
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                <h1>Clustering</h1>

                <h2>Density Based Clustering using EM algorithm</h2>
                <p>
                        Each cluster is assumed to have a probability density with certain parameters (e.g. Multivariate Gaussian). The goal of Density Based clustering is to determine the number of such model components (i.e. clusters) in a data set, and the parameters of the probability density of each component. Once the components of the whole data set are determined, a Density Based cluster may indicate the probability of each variable belonging to a particular cluster. Number of clusters is determined using cross-validation. Each variable has a probability distributiona indicating the probability of the variable belonging to each of the clusters.
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                <h4>Method parameters</h4>
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                        <dt>Data files</dt>
                        <dd>Raw data files correspondent to the samples selected to bi in the projection plot.</dd>

                        <dt>Colouring style</dt>
                        <dd>The dots corresponding to every sample can be colored depending on the sample's parameter state or on the file.</dd>

                        <dt>Peak measuring approach</dt>
                        <dd>It can take two values: height or area. The projections will be calculated using one of this two values.</dd>

                        <dt>Peaks</dt>
                        <dd>Peaks that will be taken into account to create the projection plot.</dd>

                        <dt>Visualization</dt>
                        <dd>The visualization of the result of non hierarchical clustering algorithms can be performed using PCA or Sammon's projection</dd>

                        <dt>Type of data</dt>
                        <dd>It can take two values: Samples or variables. The clustering will be applied to one of this types of data.</dd>

                        <dt>Algorithm</dt>
                        <dd>Algorithm that will be used to cluster the data.</dd>

                        <dt>Link type</dt>
                        <dd>This parameters is only enable when the hierarchical clustering has been chosen. The distances between clusters is determined by the chosen linkage.</dd>

                        <dt>Distance fuction</dt>
                        <dd>This parameters is only enable when the hierarchical clustering has been chosen. The distances between points is determined by the chosen distance function. </dd>

                        <dt>Number of groups</dt>
                        <dd>The number of clusters has to be defined by the user in advance for some clustering algorithms. This parameter is available only when K-means or Farthest First algorithm are chosen. </dd>
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